1. Field of the Invention
Generally, the present invention relates to manufacturing processes, and, more particularly, to the modeling of product streams in a manufacturing environment, such as a semiconductor facility, in which a plurality of different product types and processes and metrology tools are handled.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in industrial fields, in which highly complex process tools operate on complex products according to specified process parameters that may vary between different product types. A prominent example in this respect represents the field of semiconductor device fabrication, since, here, it is essential to combine cutting-edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve process tool utilization. The latter aspect is especially important since, in modern semiconductor facilities, equipment is required which is extremely cost-intensive and represents the dominant part of the total production costs.
As one example for a mass product, integrated circuits are typically manufactured in automated or semi-automated facilities, thereby passing through a large number of process and metrology steps to complete the device. The number and the type of process steps and metrology steps a product, such as a semiconductor device, has to go through depends on the specifics of the product to be fabricated. For example, a typical process flow for an integrated circuit may include a plurality of photolithography steps to image a circuit pattern for a specific device layer into a resist layer, which is subsequently patterned to form a resist mask for further processes for structuring the device layer under consideration by, for example, etch or implant processes, deposition processes, heat treatments, cleaning processes and the like. Thus, layer after layer, a plurality of process steps are performed based on a specific lithographic mask set for the various layers of the specified device. For instance, a sophisticated CPU requires several hundred process steps, each of which has to be carried out within specified process margins so as to fulfill the specifications for the device under consideration. Since many of these processes are very critical, a plurality of metrology steps have to be performed to efficiently control the quality of the process flow. Typical metrology processes may include the measurement of layer thickness, the determination of dimensions of critical features, such as the gate length of transistors, the measurement of dopant profiles and the like. As the majority of the process margins are device-specific, many of the metrology processes and the actual manufacturing processes are specifically designed for the device under consideration and require specific parameter settings at the adequate metrology and process tools.
In many production plants, such as semiconductor facilities, a plurality of different product types are usually manufactured at the same time, such as memory chips of different design and storage capacity, CPUs of different design and operating speed and the like, wherein the number of different product types may even reach a hundred and more in production lines for manufacturing ASICs (application specific ICs). Since each of the different product types may require a specific process flow, specific settings in the various process tools, such as different mask sets for the lithography, different process parameters for deposition tools, etch tools, implantation tools, chemical mechanical polishing (CMP) tools, furnaces and the like, may be necessary. Consequently, a plurality of different tool parameter settings and product types may be simultaneously encountered in a manufacturing environment.
Hereinafter, the parameter setting for a specific process in a specified process tool or metrology or inspection tool may be commonly referred to as process recipe or simply as recipe. Thus, a large number of different process recipes, even for the same type of process tools, may be required which have to be applied to the process tools at the time the corresponding product types are to be processed in the respective tools. However, the sequence of process recipes performed in process and metrology tools or in functionally combined equipment groups, as well as the recipes themselves, may have to be frequently altered due to fast product changes and highly variable processes involved. As a consequence, the tool performance, especially in terms of throughput, is a very critical manufacturing parameter as it significantly affects the overall production costs of the individual products. Therefore, in the field of semiconductor production, various strategies are practiced in an attempt to optimize the stream of products for achieving a high yield with a moderate consumption of raw materials. In semiconductor plants, substrates are usually handled in groups, called lots, wherein, in a frequently encountered strategy, the dispatching of a sequence of lots for a given group of process tools, in which at least a part of the manufacturing process is to be performed, is determined on the basis of the current state of the lots and the tools such that an efficient processing of the lots may be achieved. Thus, a so-called dispatch list may be established when demanded by an operator or an automated supervising system, which may describe the sequence of releasing the various lots in an attempt to obtain efficient routing of the released lots through the process flow under consideration.
Another approach for generating an efficient stream of products through a manufacturing environment is referred to as scheduling and includes the calculation of a schedule for the lots and process tools over a certain time interval or time horizon into the future. Based on the current tool and lot status and using predefined functions with respect to manufacturing specific criteria, the schedule may be “optimized,” wherein, however, changes of the manufacturing environment, in terms of tool availability, process recipe changes and the like, may require frequent updating of the schedule, wherein the consideration of all relevant constraints and process criteria, such as an efficient handling of so-called re-entrant processes, in which products are repeatedly processed in the same process tools, however, at different stages of the manufacturing process, may not be efficiently handled by conventional strategies, thereby reducing the effect of the schedule for enhancing the productivity in the manufacturing environment under consideration.
Moreover, it is frequently important to estimate the investments in terms of resources, such as process and metrology tools, for a manufacturing environment on the basis of a given product entry rate in order to obtain a prediction for installing or re-installing a manufacturing environment. For this purpose, complex software tools are available that include a capacity-based model of the manufacturing environment. Based on the given start rate of a specified product type mixture, the model may then estimate the required resources.
FIG. 1 schematically illustrates a typical conventional flow 100 for modeling the number of process tools in a manufacturing environment, such as a semiconductor facility, on the basis of a desired production rate. In box 110, the desired start rate for one or more product types A . . . Z to be processed in the manufacturing environment under consideration may be defined. For example, in a semiconductor facility, various types of microprocessors are to be manufactured on the basis of substantially predefined process recipes. Consequently, for each individual type of microprocessor, a desired start rate, for example in the form of wafer count per time, may be entered. In box 120, the capacity-based model of the manufacturing environment under consideration may calculate, on the basis of the specific process recipes and predefined tool capacities, that is, throughput values of a process tool or tool group for a given process recipe, the resources, i.e., the number of individual tools per tool group I . . . N, required for providing the capacity of running at the desired start rate for each specified product type. Finally, the calculated capacity is output in box 130, for instance in the form a tool count for the various process and metrology tools of the manufacturing environment under consideration.
Thus, the product stream in existing or virtual manufacturing environments may be calculated on the basis of the above-described techniques. However, in some respects, these techniques may suffer from reduced flexibility with respect to efficiently determining a cost-efficient product stream, since, for example, the schedule calculation described above may require immense computational resources when a complex manufacturing environment is considered, while otherwise non-acceptable simulation intervals may be needed, which significantly reduce the applicability of these techniques with respect to increasing efficiency of resources of a manufacturing environment. On the other hand, the strategy described with reference to FIG. 1 may not allow the direct deduction of a product entry rate that would be supported by a given resource capacity, such as a given equipment set. In order to obtain a corresponding estimation of a supported start rate, a plurality of simulation runs on the basis of different product start rates may have to be performed in an attempt to get a result from the model that is identical or at least close to the given tool count. Moreover, when a moderately complex mixture of product types is to be processed in the manufacturing environment, a corresponding large number of variables may have to be varied for the various simulation runs, thereby rendering this technique less attractive due to the moderately long simulation times.
In view of the situation described above, there is therefore a need for a technique that enhances the efficiency of a production process while avoiding or reducing one or more of the problems identified above.